Multi-tiered transportation identification system

ABSTRACT

A system for identifying an aspect of interest on a vehicle that includes a local AI system that can analyze sensor data from an on-site sensor to make an attempt to identify the aspect of interest according to first criterion. The aspect of interest can be information printed on the vehicle and/or on a seal of the vehicle. If the local AI system is unable to identify and validate the information on the first effort, it can consult with a central/global AI system that can leverage its own database and other local systems at other locations for subsequent attempts at identifying and validating the aspects of interest.

This application is a continuation of U.S. patent application Ser. No.17/866,943, filed Jul. 18, 2022. U.S. patent application Ser. No.17/866,943 and all other extrinsic references contained herein areincorporated by reference in their entirety

FIELD OF THE INVENTION

The field of the invention is information identification andverification for transportation.

BACKGROUND

The background description includes information that may be useful inunderstanding the present invention. It is not an admission that any ofthe information provided herein is prior art or relevant to thepresently claimed invention, or that any publication specifically orimplicitly referenced is prior art.

A critical part of transporting containers is ensuring their integrityfrom the point of origin to their destination. For example, a trucktrailer or container that was loaded and shut in Tampa, Fla. must remainunopened along the route until it arrives at its unloading destinationin Toronto, Canada. To do so, seals are attached to the doors of acontainer once the container is shut. The integrity of this seal is thenchecked at the point of destination for tampering, and the seal is alsochecked against an identifier of a truck to make sure that the entiretransport system (tractor plus trailer/container) is consistent with theinformation at departure.

Unfortunately, reviewing identifying information such as trailernumbers, seals and seal numbers at the point of arrival istime-consuming. This leads to back-ups of trucks at a receiving site,which results in wastes of time and fuel as trucks wait to be admittedand verified. Additionally, in certain areas having a line of trucksoutside of a facility can be dangerous as some of these receivingstations are located in dangerous areas.

The location of the seals and the identifiers for a truck and/orcontainer is not industry standard. It can vary from company to company,from manufacturer to manufacturer, and even from journey to journey.Numbers printed on a tractor or a trailer can be vertically arranged,can be of different sizes and colors, of different fonts, etc. Seals arenot universal either. All of this presents a challenge in automationbecause this information must first be found before it can be used toidentify and validate the truck and the integrity of the shipment.

Moreover, it is important to be able to track the progress of a truckand container as it makes its journey from origin to destination, as itmight stop at stations along the way to change drivers, or trucktractors.

Thus, there is still a need for a system that can rapidly and accuratelylocate and verify necessary information on a truck while accounting forthese variables, and that can leverage knowledge gathered across anetwork of locations.

SUMMARY OF THE INVENTION

The inventive subject matter provides apparatus, systems and methods inwhich an artificial intelligence (“AI”) system can identify an aspect ofinterest of a target. A local AI system uses a first sensor to derive afirst set of sensor data from an environment that contains the target.Then, the local AI system analyzes the sensor data to make a firstattempt at identifying the aspect of the target. The system thendetermines whether the results of this first attempt satisfy a criterion(e.g., a level of confidence, an accuracy, etc.). If the results of thefirst effort fail to satisfy the criterion, then at least some of thefirst set of sensor data is provided to a global AI system to make asecond effort at identifying the aspect of the target.

In embodiments, the target is a tag or seal on a truck and the aspect ofthe target is code (e.g., alphanumeric code or other sequence of digits)printed on the tag.

In embodiments, the global AI system is programmed to utilize a secondsensor to obtain second sensor data if the second effort fails to meet acorresponding second criterion, and the global AI system makes a thirdeffort to identify the aspect of the target with this second sensor data(and optionally, some of the first set of sensor data).

In these embodiments, the global AI system is programmed to instruct thesecond sensor to change positions and/or vantage points by directing amovement of the second sensor via an actuator system of the secondsensor.

In these embodiments, the first and/or second sensors can be camerasystems that have actuators that allow the camera to pivot verticallyand pan horizontally, as well as zoom capabilities.

In a variation of these embodiments one or both of the second sensor canbe cameras mounted on drones. In these embodiments, the actuator systemcan collectively refer to flight controls of the drone as well controlover features of the camera itself (e.g., zoom, focus, etc.).

Various objects, features, aspects and advantages of the inventivesubject matter will become more apparent from the following detaileddescription of preferred embodiments, along with the accompanyingdrawing figures in which like numerals represent like components.

All publications identified herein are incorporated by reference to thesame extent as if each individual publication or patent application werespecifically and individually indicated to be incorporated by reference.Where a definition or use of a term in an incorporated reference isinconsistent or contrary to the definition of that term provided herein,the definition of that term provided herein applies and the definitionof that term in the reference does not apply.

The following description includes information that may be useful inunderstanding the present invention. It is not an admission that any ofthe information provided herein is prior art or relevant to thepresently claimed invention, or that any publication specifically orimplicitly referenced is prior art.

In some embodiments, the numbers expressing quantities of ingredients,properties such as concentration, reaction conditions, and so forth,used to describe and claim certain embodiments of the invention are tobe understood as being modified in some instances by the term “about.”Accordingly, in some embodiments, the numerical parameters set forth inthe written description and attached claims are approximations that canvary depending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the invention are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable. The numerical values presented in some embodiments of theinvention may contain certain errors necessarily resulting from thestandard deviation found in their respective testing measurements.

Unless the context dictates the contrary, all ranges set forth hereinshould be interpreted as being inclusive of their endpoints andopen-ended ranges should be interpreted to include only commerciallypractical values. Similarly, all lists of values should be considered asinclusive of intermediate values unless the context indicates thecontrary.

As used in the description herein and throughout the claims that follow,the meaning of “a,” “an,” and “the” includes plural reference unless thecontext clearly dictates otherwise. Also, as used in the descriptionherein, the meaning of “in” includes “in” and “on” unless the contextclearly dictates otherwise.

The recitation of ranges of values herein is merely intended to serve asa shorthand method of referring individually to each separate valuefalling within the range. Unless otherwise indicated herein, eachindividual value is incorporated into the specification as if it wereindividually recited herein. All methods described herein can beperformed in any suitable order unless otherwise indicated herein orotherwise clearly contradicted by context. The use of any and allexamples, or exemplary language (e.g. “such as”) provided with respectto certain embodiments herein is intended merely to better illuminatethe invention and does not pose a limitation on the scope of theinvention otherwise claimed. No language in the specification should beconstrued as indicating any non-claimed element essential to thepractice of the invention.

Groupings of alternative elements or embodiments of the inventiondisclosed herein are not to be construed as limitations. Each groupmember can be referred to and claimed individually or in any combinationwith other members of the group or other elements found herein. One ormore members of a group can be included in, or deleted from, a group forreasons of convenience and/or patentability. When any such inclusion ordeletion occurs, the specification is herein deemed to contain the groupas modified thus fulfilling the written description of all Markushgroups used in the appended claims.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 is a diagrammatic overview of the components of a systemaccording to embodiments of the inventive subject matter.

FIG. 2 is a flowchart of processes according to the inventive subjectmatter.

FIGS. 3A-3E are perspective views from the camera as it searches for anaspect of interest on a trailer.

FIGS. 4A-4B are examples of a tractor and a trailer, showing possiblelocations of information relevant to the systems and methods of theinventive subject matter.

FIG. 5 shows examples of different types of seals and the materials thatcan be printed on the seals.

FIG. 6 is a flowchart of another process executed by the system,according to embodiments of the inventive subject matter.

DETAILED DESCRIPTION

Throughout the following discussion, numerous references will be maderegarding servers, services, interfaces, engines, modules, clients,peers, portals, platforms, or other systems formed from computingdevices. It should be appreciated that the use of such terms, is deemedto represent one or more computing devices having at least one processor(e.g., ASIC, FPGA, DSP, x86, ARM, ColdFire, GPU, multi-core processors,etc.) programmed to execute software instructions stored on a computerreadable tangible, non-transitory medium (e.g., hard drive, solid statedrive, RAM, flash, ROM, etc.). For example, a server can include one ormore computers operating as a web server, database server, or other typeof computer server in a manner to fulfill described roles,responsibilities, or functions. One should further appreciate thedisclosed computer-based algorithms, processes, methods, or other typesof instruction sets can be embodied as a computer program productcomprising a non-transitory, tangible computer readable media storingthe instructions that cause a processor to execute the disclosed steps.The various servers, systems, databases, or interfaces can exchange datausing standardized protocols or algorithms, possibly based on HTTP,HTTPS, AES, public-private key exchanges, web service APIs, knownfinancial transaction protocols, or other electronic informationexchanging methods. Data exchanges can be conducted over apacket-switched network, the Internet, LAN, WAN, VPN, or other type ofpacket switched network.

The following discussion provides many example embodiments of theinventive subject matter. Although each embodiment represents a singlecombination of inventive elements, the inventive subject matter isconsidered to include all possible combinations of the disclosedelements. Thus if one embodiment comprises elements A, B, and C, and asecond embodiment comprises elements B and D, then the inventive subjectmatter is also considered to include other remaining combinations of A,B, C, or D, even if not explicitly disclosed.

As used herein, and unless the context dictates otherwise, the term“coupled to” is intended to include both direct coupling (in which twoelements that are coupled to each other contact each other) and indirectcoupling (in which at least one additional element is located betweenthe two elements). Therefore, the terms “coupled to” and “coupled with”are used synonymously.

FIG. 1 shows an overview of the components of a system 100 according toembodiments of the inventive subject matter.

FIG. 1 shows a plurality of locations 110A-110C. Each of these locations110 could be a point of origin for vehicle 140 such as a truck or othervehicle that can be closed to carry cargo or carry a shipping container,a destination point, or an intermediate point along a journey from apoint of origin to a destination. Here, only three locations 110 areshown, but it is understood that many other locations could be a part ofthe system 100.

At each location, there is a local artificial intelligence (“AI”) system120 that is communicatively coupled with one or more sensors 130 at therespective location 110, and also communicatively connected with aglobal AI system 150 that is coupled to other local AIs 120 at otherlocations 110.

One or more of the local AI systems 120 can include or be incommunication with a respective local database driven system (DDS) thatcan store information locally. The DDS can include one or more computersystems that include processors and physical memory that can house thedatabases contained therein. The global AI system 150 can also includeor in communication with its own DDS that houses information relevant tonetwork-wide functions, including assisting local AI systems 120 withqueries. In embodiments, the database managed or belonging to the globalAI system 150 is significantly larger than the local databases of thelocal AI systems 120. For example, the database of a given local AIsystem 120 could be limited (either artificially, or by actual availableresources) to a particular period of time's worth of accumulated data(e.g., 20 days, 30 days, 60 days, etc.) and/or could be limited tostoring only locally-relevant information (e.g., only information abouttrucks/companies that have actually passed through the particularlocation 110). In contrast, the database of global AI system 150 isconsidered to be a global repository of information for future use thatcan include information about companies, different trucks, trailers,seals, and any other relevant information that can assist in the futureidentification of a truck at a location 110.

Local AI 120 can be a computer system or groups of computer systems thatare local to the location 110. The local AI 120 can include cloudcomponents for storage (such as parts of the DDS), however it is to beunderstood that at least a portion of the local AI 120 is physicallylocated on site at the location 110.

The global AI system 150 is considered to be a computer system or groupsof computer systems remotely located from the locations 110. Inembodiments, the global AI system 150 could be local to one of thelocations 110 but would still be a central hub for all of the local AI120 of the network of locations 110. The global AI system 150 can belocated partially or entirely on the cloud.

The local AI system 120 and global AI system 150 are each considered tohave underlying physical computer system(s) that have at least oneprocessor, memory, communications interfaces for data exchange, userinterfaces for data input and output (e.g., via keyboards, mice, touchscreen, voice input, screens for video and image output, etc).

Typically, the sensor 130 at the location 110 is a camera capable ofcapturing still images, video and sound, but other sensors can be usedas well.

When a truck 140 arrives at a location 110, the sensor 130 capturessensor data that is then provided to local AI 120.

For example, the truck 140 is instructed to park at or pass through anarea that is within the view of a camera 130.

At the location 110, the environment can have fiducial markers that arewithin the default view of the camera 130, to be used for calibration ofthe system and can be used for reference in inclement weather orvisibility situations. In embodiments, the fiducial markers can bebrightly colored landmarks, such as yellow posts that are erected in thevisible environment at the camera's default view (i.e., default pan,tilt, zoom and other settings). A reference image is taken during aclear, good weather condition. This image can be used as a reference forcomparison in situations of inclement weather where visibility islimited by comparing the reference image to the real-time reducedvisibility images and finding the location of the fiducial markers ineach. This way, the system can properly calibrate the camera andmaintain spatial awareness in the reduced-visibility situations.

FIG. 2 shows a flowchart of the processes according to embodiments ofthe inventive subject matter. While the description refers to theprocesses occurring at location 110A, it is understood that theseprocesses could be carried out at any of the locations 110 of thenetwork for trucks 140 arriving there via their respective local AI 120.

At step 210, a sensor 130A (in this example, a camera), begins obtainingsensor data in the form of image data (which can be still or video, andwith or without sound) that includes a vehicle 140A (in this case, atruck 140A having a trailer).

At step 220, the local AI system 120A that is local to the sensor 130Abegins to analyze the obtained sensor data as a first effort to identifyan aspect of interest of the truck 140A.

The aspect of interest can generally be one or more items of informationthat are to be gathered from the truck 140A. Examples of aspects ofinterest can include a tractor number, a trailer number, registrationnumbers, a seal presence, a seal number etc. In this example, theaspects of interest sought are a trailer number and a seal number.

In embodiments, the target generally can include a tag or seal attachedto a truck 140A. In these embodiments, the aspect of interest would beinformation printed on the seal/tag. The information can be alphanumericdigits, a machine-readable code (e.g., QR code, barcode, etc.), or otherinformation.

Thus, at step 220, the local AI 120A analyzes the image data from thecamera 130A to try and find the aspects of interest on truck 140A.

To try and find the information of interest, such as on a truck 140A,the local AI 120A can analyze the image data and then control the sensor130A such that it can look for the information and zoom in on potentialareas of interest. In these embodiments, the sensor 130A (e.g., acamera) can have pan-tilt-zoom (“PTZ”) capabilities that can becontrolled by the local AI 120A in order to try to obtain and identifythe aspect(s) of the target of interest.

FIG. 3A shows a default view by a camera 130A. This image data showsmost of a truck 140A, and the local AI 120A in this situation must finda trailer number and a seal in order to be able to verify the truck140A. However, in its default view, the camera 130A is set for a truckpulling a normal-sized trailer. Because this is a long trailer, part ofthe trailer is outside of the view of this image data. In the view ofFIG. 3A, a seal (and thus a seal number) is not visible. Likewise, onlythe first couple of digits of the trailer number 141 (the numbers “31”)are visible.

One challenge to obtaining these aspects of interest is that while thereare governmental labeling requirements for commercial vehicles, thereexists the potential for great variation in how these labels aredisplayed. Additionally, in certain situations, the sensor 130A isobtaining the sensor data while the truck 140A is moving. FIGS. 4A-4Bprovide an illustrative example of a tractor 410 and trailer 420 thatshow some of the items of information and their possible locations.

As seen in FIG. 4A, the tractor 410 can include information such as atruck number 411, regulation number 412 (which can include USDOTnumbers, VIN numbers, etc.) and a fire extinguisher decal 413. However,the commercial labeling requirements only specify that a label must beon both sides of a vehicle, must be a contrasting or bold color, andmust be a minimum of 2 inches tall. There is no standard font, size,color, background color, etc. for these labels. For example, the trucknumber 411 must generally be on the hood and to the side of the truck,but there's no standard placement for it that can be relied uponconsistently.

Likewise, the trailer 420 of FIG. 4B can include a horizontal trailernumber 421, a vertical trailer number 422, trailer lettering 423, and anoverall length label 424, and graphics 425 but there is significantvariance on where some of these items of information may be. Some, suchas the graphics 425, may not even be required. And for those that mustbe there—like the trailer number 422, there's no regulation as towhether the number must be, nor its font or color, nor whether it mustbe written vertically or horizontally.

Additionally, there are a near infinite number of ways that seal numberscan be displayed because there is no standardization for the seals orthe seal numbers. FIG. 5 shows three types of common seals 510, 520 and530 that have information 511, 521, 531 printed on them, respectively.These are only three example among many possible seals that could beused in the industry, and serves to illustrate just how different theseseals (and the way the information is imprinted on them) can be. Itshould be noted that in addition to or instead of the printedinformation, a seal could have embedded WiFi, RFID or othercommunication capability that would allow it to exchange datawirelessly. Using the techniques discussed herein, the system can learnto identify seals that contain these capabilities and, upon detectingthem at a location 110, the local AI 120 can communicate this torelevant system to establish a communication with the seal.

At step 230, the local AI 120A determines whether the first attempt ofstep 220 satisfies a first criterion (or first collection of criteria).The first criterion can be a reliability criterion or an accuracycriterion. A reliability criterion (otherwise known as a confidencecriterion) is a confidence that a particular identified feature orinformation has been properly located or identified. For example, if itis known that a truck has a particular color and that it is from aparticular company, then from historical information (encounters ofsimilar trucks with this company) it is likely to a certain determinedpercentage that the tractor number is in the front, will be of blacktext and will have particular prefix as part of its identificationnumber. An accuracy criterion is a measure of how accurately a featurehas been identified. This can be based on a comparison of the identifiedfeature against a reference number in a database, for example.

In embodiments, the first criterion/criteria can be (or additionallyinclude) a visibility criterion based on whether or not the targetobject is partially obscured in the image data.

This step can be repeated as the local AI 120A can make corrections byissuing commands to the PTZ actuators of the camera 130A. Thus, afteranalyzing the image of FIG. 3A, the local AI 120A does not find anyaspects of interest (e.g., a seal and seal number, a trailer number)such that the first criterion is satisfied. However, before handing offthe process to the AI system 150, the local AI 120A can control thecamera 130A to move to get a better view. In this case, the local AI120A has a priori been programmed to understand that if a trailer's rearis partially or fully off camera, then it is most likely to the right ofthe camera view because of the physical position of the camera 130Arelative to the space for receiving a truck 140A.

In embodiments, the a priori programming can be programmed at systeminstall, where the system can be calibrated and programmed to accountfor known conditions such as the relative position of the camera 130Arelative to the driveway where the trucks are likely to pass, the heightof the camera, ambient lighting during the day and/or night, etc.

In embodiments, the a priori programming can be obtained by the local AI120A learning about the environment via trial and error. For example,over time the local AI 120A can attempt to pan, tilt and zoom indifferent forms until it achieves success. As these attempts aggregate,the local AI 120A learns via machine learning techniques where theinformation it is looking for is most likely to be relative to thedefault frame, and it can start by panning/tilting in that direction. Atfirst, the system will likely have to move the camera in various waysuntil it acquires enough attempts to make a meaningful prediction on thelikely camera adjustments required.

Thus, the local AI 120A issues a command to the camera 130A to pan tothe right. It does so, and results in the view of FIG. 3B. In FIG. 3B,the trailer number 141 is clearly visible and is recognized by the localAI 120A (via image recognition techniques) to a sufficient level suchthat the first criterion is satisfied.

However, the first criterion regarding information on a seal is notsufficiently visible at this step. Applying image recognitiontechniques, the local AI 120A is able to recognize the door latch 142 ofthe trailer. From past attempts on many other trucks, the local AI 120Ahas learned (via machine learning techniques) that tags and seals aremost likely attached to the door latch of a trailer. Correspondingly,the local AI 120A commands the actuators of camera 130A to tilt downwardto center the field of view over the trailer door latch 142, as seen inFIG. 3C.

However, the view of FIG. 3C is still too far for the local AI 120A tobe able to find and identify a seal. Thus, the local AI 120A commandsthe camera 130A to zoom in, which is shown in FIG. 3D. However, the sealis not visible in FIG. 3D. Because the local AI 120A “knows” (from prioranalysis of other trucks that have come through in the past or fromprior consultation with the global AI 150) the seal is typicallyattached to the latch 142 of the trailer. By applying image recognitiontechniques, the local AI 120A determines that the latch 142 ispartially, but not completely, within the image. Therefore, the local AI120A commands the camera 130A to tilt further down and then zoom intothe area of the latch 142, as shown in FIG. 3E. In FIG. 3E, the seal 143attached to latch 142 is visible as well as a tag 144.

In embodiments, the local AI 120 has some a priori knowledge of one ormore of the truck, the trucking company, and the seal used by thetrucking company, that it can apply in the search for the aspects ofinterest. For example, if the local AI 120 already has data thatindicates that the seals used by a particular trucking company areyellow, the local AI 120 the puts greater weight on parts of the imagethat are yellow or close to yellow in the RGB spectrum as places tosearch.

In this example, if the seal 143 itself is the aspect of interest, thenmerely being able to find it and be able to confirm that the seal is inplace to a sufficient degree of confidence (e.g., a probability based onimage recognition that the seal is in place) satisfies the criterion. Inthis case, the tag 144 is not of interest and the local AI 120disregards it.

Thus, if the first criterion is satisfied after the first effort, thelocal AI 120A approves the truck 140A based on the trailer number 141and seal 143 and the truck is allowed to enter the facility at step 231.The results of the search (e.g., the identifying information such as thetrailer number, visual characteristics of the seal, location of the sealand other identifying information on the trailer and truck, etc.) can beprovided to the global AI system 150 so that this information can beused with or provided to local AI 120 s of other locations 110 foridentification of similar trucks or for this company at these locations.

If this is the first time that the local AI 120A has successfully foundthe seal 143 for this trucking company, the local AI 120A can recordthis information. For example, if the type of seal was previously knowfor this trucking company but not the color, the local AI 120A can storethat this trucking company uses a seal of this color (e.g., yellow).This information can also be provided to the global AI system 150 forlong-term storage and/or to share with other locations.

If, in the example of FIGS. 3A-3E, the aspect of interest is informationprinted on the seal 143 and not just the seal 143 itself, then the firstcriterion would not be satisfied on this first effort because there isno information visible on the seal 143 in the image of FIG. 3E, andthere are no additional views that would be useful.

If the first criteria is not met, the local AI 120A is programmed tocontact the global AI system 150 for assistance. At step 240, the localAI 120A provides at least some of the sensor data (in this example,image data from one or more of the images of FIGS. 3A-3E) from sensor130A to the AI system 150. The local AI 120A can also provide additionalinformation that it has gathered. Thus, if it successfully determinedthe trailer number 141, the local AI 120A could also provide thisinformation to the AI system 150.

The first attempt by the local AI 120A could fail to meet the firstcriterion for several reasons. For example, if the local AI 120A has notanalyzed a truck from a particular company before, it would not haveprior knowledge of things like the type of seal that particular companyuses. In another situation, some of the aspect of interest could beobscured by mud, dirt, or snow. In yet another situation, the weather atthe location 110A could limit visibility such that the image data fromthe camera 130A is not of a sufficient quality for the local AI 120A toclear the criteria thresholds for the first criterion.

In embodiments, the local AI 120A can be programmed to spend a certainamount of time on the attempt and/or be limited to a certain amount ofadjustments to the camera 130A before determining the first attempt hasfailed. This way, the local AI 120A can escalate the situation to the AIsystem 150 without unduly delaying the truck 140A, other trucks thatmight be in line, and the station 110A. The time limit and/or attemptlimit does not require the local AI 120A to go through the process allthe way to FIG. 3E. It may be that the local AI 120A makes a very quickinitial scan of the image data at the default setting or only make oneor two adjustments to the camera view before proceeding to the AI system150.

At step 250, the global AI system 150 then proceeds to make a secondeffort to identify the aspects of interest of the target 140A. Thesecond effort can be performed according to the first criterion as well.However, in preferred embodiments, the second effort is performedaccording to a second criterion (or second collection of criteria).

The second criterion can be a reliability criterion or an accuracycriterion. In embodiments the second criterion can be of the same typebut of a different value than the first criterion. For example, thesecond criterion can be a different reliability threshold than the firstcriterion. In other embodiments, the second criterion can be of adifferent type than the first criterion. For example, if the firstcriterion is a threshold of accuracy, then the second criterion could bea threshold of reliability.

In embodiments, the global AI system 150 can recognize at least one ofthe aspects of interest to get a preliminary result. This preliminaryresult can be one of the aspects of interest that was sought by thelocal AI 120A or a different set of information. For example, if thelocal AI 120A searched for a trailer number and a seal, the global AIsystem 150 can first look for the trailer number. Alternatively, oradditionally, the global AI system 150 can be programmed to look for alicense plate number, a company name, or other identifying informationfor the truck and/or the trailer that is included in the image dataprovided by the local AI 120A. The AI system 150 can then query anotherof the local AI 120 (e.g., local AI 120B and 120C and other local AI 120s) in the network, which can include local AI 120 at locations 110 alongthe route traveled by truck 140A to see if any of the other local AI 120have image data of a higher quality (e.g., focus, higher resolution,closer distance, etc.) than the image data capture by camera 130A.

In this example, the higher-quality image data can be analyzed by theglobal AI system 150 using image recognition techniques to obtain theremaining aspects of interest. For example, using this higher qualityimagery, the AI system 150 could determine that the truck 140A did havea seal attached during the journey. Based on positively identifying aseal, the AI system 150 can return information to the local AI 120A thattells the local AI 120A where on the trailer to search for the seal.

The local AI 120A can provide information that it determined that doesnot meet the first criterion. For example, if the local AI 120A deriveda number from the image data that corresponds to the trailer number 141,but could not find a match for this trailer number, the local AI 120Acan still provide the derived trailer number 141 to the AI system 150.This can help the system determine a correct location of a trailernumber in situations where a different identification number (such asdepartment of transportation ID number) is mis-read as a trailer number.

In this example, the second effort by the AI system 150 can include areview of the number against known trailer number formats and/or actualtrailer numbers. This can be performed against a database stored by theAI system 150. Alternatively and/or additionally, the global AI system150 can query other local AI 120 at other locations to determine whetherthere is a match in format or actual trailer number. These other localAI 120 then report their findings back to the AI system 150. This reviewcan also be performed by the global AI system 150 and/or other local AI120 for other numbers that could match the provided number, either viaan approximate or exact match of alphanumeric characters or in format(by length, prefixes, etc.). Thus, if this review returns a result of adifferent type of identification number (e.g., DOT number, or otheridentifier), the AI system 150 can instruct the local AI 120A to lookelsewhere for the trailer number.

If the other local AI 120 at other locations have the informationrequested by the AI system 150 (e.g., photographs of the truck along theroute, a match of a trailer number, etc.), they provide it along withother relevant information back to the AI system 150. The relevantinformation can be information that is associated with the requestedinformation, that could be linked or otherwise associated at one or moreof the local databases of the local AI 120 at other locations. Forexample, if the AI system 150 requested a search for a trailer ID numberor trailer number formats to try to identify the company associated withthe trailer, a local AI 120 could provide the appropriate number matchor trailer number format (if found), as well as a company name and/or aprobable location of a seal for trailers of this company.

Thus, the preliminary result could be a confirmation of some of, but notall of the aspects of interest sought by the local AI 120A.

In embodiments, if the second effort by the AI system 150 succeeds, theAI system 150 sends information to the local AI 120A to assist the localAI 120A in a future identification of the aspect of the target. This caninclude associations between a trucking company and the locations wherean identifier and/or a seal is most likely to be, additional serialnumbers associated with one or more companies, or other information.

In some embodiments, the second effort can be determining a search areaof a truck or trailer that satisfies a second criterion for confidenceof a search in this area. For example, a result of consulting the AIsystem 150's database and/or consulting with other local AI 120 of otherlocations 110 can return a result that the aspect of interest sought(e.g., the seal containing the seal information) is likely to be foundat a particular area of the trailer 140. This information can be relayedto the local AI 120A with commands for camera 130 to expand the search.If the local AI 120A is unable to locate the seal and information suchthat the criterion for the seal and its information is met, then thelocal AI 120A can re-consult with the AI system 150 and the process canrepeat using one or more of the approaches discussed herein. In avariation of these embodiments, the AI system 150 can be providedcontrol of the camera 130 directly, such that the AI system 150 canobtain additional image data based on the results of its analysis and/orthe information it receives in response from other local AI 120. Inthese embodiments, the AI system 150 can then instruct the camera 130A,via the camera's PTZ actuators, to capture additional areas of the trucksuch that a better determination can be made.

In embodiments, information associated with the first attempt that meetssome of the criteria of the first attempt is provided by the local AI120A to the AI system 150. For example, if the local AI 120Asuccessfully detected the trailer number but still needs to determinethat the seal is present (and determine the seal number), the local AI120A can provide the obtained trailer number. In response to this, theAI system 150 can search its own database and/or consult with otherlocal AI 120 for the missing information.

In these examples, the AI system 150 and/or the other local AI 120 atother locations 110 can perform the requisite analysis by conductingimage recognition on previously-captured images of trucks at thoselocations, and statistically compiling the results. From this, thesystems can create associations based on the statistical commonalitiesof trucks. For example, from this, the systems can store associationsbetween tractor colors and trucking companies, trailer colors andcompanies, companies and common locations of the different types ofidentifying information, companies and the typical types of seals used,the companies and/or trailer types and the typical seal locations, andother associations. This can be performed over time and a databaseconstructed that then stores the data and the respective associations,such that when a query from local AI 120A is provided to the global AIsystem 150, the AI system 150 and/or the other local AI 120 can rapidlyconduct an analysis and provide a response.

If the second effort by the AI system 150 succeeds, the AI system 150sends, at step 260, the results of the second effort to the local AI120A that can include a confirmation that the aspects of interestinitially sought by the local AI 120A. If there is no additional aspectsof interest to find and detect, the local AI 120A verifies the trailerand its integrity (because of the located seal) and allows the truck 140to enter the facility 110A at step 231.

If the second effort by the AI system 150 fails, the AI system 150 can,at step 270 send an instruction to a second sensor 131 at the location110A that causes the second sensor 131 to provide sensor data regardingthe target 140A. The second sensor 131 can be the same type of sensor assensor 130A (e.g., a second camera) or a different type of sensor thansensor 130A (e.g., if sensor 130A is a camera attached to a physicalstructure, sensor 131 can be a microphone, or a camera with thermal orinfrared vision, or a temperature monitor, or a camera on a drone,etc.). Then, using the second sensor data, the AI system 150 can make athird attempt to identify an aspect of the target based on the secondsensor data. The third attempt can include analyzing the second sensordata in a similar manner to the first sensor data, which can includeconsulting with other local AI 120 for additional information oranalysis as discussed herein.

In embodiments, the instructions sent from the AI system 150 to thesecond sensor 131 are instructions that direct the movement of anactuator system that then moves or otherwise changes the position and/ororientation of the second sensor 131.

In a variation of these embodiments, the actuator system can be a flyingdrone. In these embodiments, the second sensor can be a camera attachedto the flying drone. In these embodiments, the instructions sent fromthe AI system 150 can be flight instructions to command the drone to flyto certain locations relative to a truck 140 to enable a search for theaspects of interest.

If the third effort at step 270 is successful, the global AI system 150can report the successful information to the local AI 120A forsubsequent processing (if additional aspects of interest remain) and/orvalidation as with successes after step 230 or step 250. If the thirdeffort at step 270 is unsuccessful, the AI system 150 can retry untilsuccess is achieved. In embodiments, the AI system 150 is limited to apre-defined number of attempts. If no success is achieved, it notifiesthe local AI 120A of the failure. The local AI 120A can then notifypersonnel at location 110A of the failure for manual inspection.

The results of a manual inspection can be manually provided via acomputer at location 110A, and can be integrated into the database bythe local AI 120A and/or the AI system 150 for future use in subsequentidentification attempts.

It will be appreciated that by leveraging the information and processingcapabilities of the AI system 150 and other local AI 120, the timerequired for the process of identifying a seal and other desiredinformation on a truck is greatly reduced from that of the local AI 120Aacting alone. In practical terms, the systems and methods of theinventive subject matter allow for a real-time or near-real-timeidentification of a seal and other relevant information that was notpreviously possible. This reduces the time a truck 140 is held up beforeentry into a station 110, reducing the back-up of trucks waiting foradmission. Additionally, the time saved helps facilitate a moreefficient overall shipping process as trucks are able to get in and outof stations far more efficiently.

FIG. 6 provides a flowchart of another example process, according toembodiments of the inventive subject matter. This flowchart shows anexample of the back-and-forth communication between the local AI 120Aand the global AI system 150 that facilitates a rapid identification ofa truck and a trailer at a location 110A (for this example, a stationlocated in Toronto, Canada).

At step 610, the local AI 120A detects the truck 140A. Based on theimage data from camera 130A, the local AI 120A is able to detect thecompany name on the truck 140A (the tractor). Thus, for this aspect ofinterest, the local AI 120A has a 100% confidence that the tractor 140Ais from truck company A. The local AI 120A also locates a number that itinterprets, based on prior knowledge of where the truck numbers are forthis company, as a truck number. However, because of inclement weatherconditions, the local AI 120A only has a 90% confidence that the trucknumber is “S5” and a 10% confidence that the truck number is “55”.

At step 612, the local AI 120A consults with the global AI system 150.The AI system 150 has, stored in its database from prior network-wideinteractions with trucks from company A, a company rule that all trucknumbers for company A start with “S”. The AI system 150 then sends thisinformation back to local AI 120A, which revises its confidence to 100%confidence that the truck number is “S5” at step 614. It should be notedthat at this step, the AI system 150 could also consult with other localAI 120 of the network for information if it does not have the necessaryinformation.

At step 616, the local AI 120A detects the trailer hauled by truck S5.Based on the image data from camera 130A, the local AI 120A is able tofind a trailer number and reads it. By employing image recognitiontechniques that are known in the art, the local AI 120A determines witha 90% confidence that the trailer number is “G6” and a 10% confidencethat it is “66”. Again, the local AI 120A consults with the AI system.Suppose that the local AI system 150 does not have a trailer numberavailable (it may be this is the first time this particular trailerarrives at this particular location). It then consults with one or moreof the other local AI 120. One of the other local AI 120 reports thatthe truck with the number “S5” was seen hauling a red trailer with anumber “66” at step 618.

This is reported back to the local AI 120A, which proceeds to check thetrailer's color of the truck 140A at the station at step 620. At step622, the local AI 120A confirms that that trailer is red. The confidencelevel that the trailer is trailer “66” increases, this time to 95%.

However, because of poor weather (and thus, visibility) in Toronto, thelocal AI 120A cannot find the seal at the back of the trailer at step624. Thus, at step 626, the local AI 120A consults with the AI system150. The AI system 150 then in turn consults with the local AI 120 inMiami that provided the prior information (since it is known to thesystem that the truck was there). The local AI 120 in Miami confirms,based on its own analysis of its own captured image data, with 100%confidence the truck was seen with a seal at the bottom right corner ofthe trailer. Based on this information, the local AI 120A in Torontodirects the camera 130A to look at the bottom right corner of thetrailer and is able to find the seal and read the seal number at step628. It should be noted that this is how the local AI 120A can “know”where to start looking for trucks of this company in the future, such asin the process described above with regards to FIGS. 3A-3E.

If the seal is intact and readable, the process ends here. However, tocontinue the example, suppose that the local AI 120A finds that the sealis broken at step 630, which could indicate tampering with the cargo (apossible legal violation). The local AI 120A then consults again withthe AI system 150 which turns to the local AI 120 in Miami. The local AI120 in Miami confirms the seal was present and not tampered with at thestop there at step 632.

At step 634, the local AI 120A receives this information and theninforms personnel that the seal was tampered with somewhere betweenMiami and Toronto. If the seal number was not properly detected by thelocal AI 120A at step 628, the local AI 120A can also report this. Basedon the prior trips by this same truck, the local AI 120A can also reportthat the seal became unreadable too fast to account for normalwear-and-tear, which also indicates tampering.

In the embodiments discussed above, the local AI 120A is described ascommunicating with the AI system 150, which then reaches out to otherlocal AI 120 as the need arises. However, in certain situations, thelocal AI 120A can reach out to other local AI 120 directly when it hasbeen established that a particular local AI 120 has the informationneeded. In the example above, after discovering that the local AI 120 inMiami last saw the truck, the local AI 120A in Toronto could contact theMiami local AI 120 directly after that for information without having togo through the AI system 150.

It is contemplated that, to improve speed, a local copy of the databasehoused by AI system 150 could periodically be downloaded by the local AI120. This can be performed in advance when a particular truck's route isknown in advance so that relevant information is already availablelocally.

It should be apparent to those skilled in the art that many moremodifications besides those already described are possible withoutdeparting from the inventive concepts herein. The inventive subjectmatter, therefore, is not to be restricted except in the spirit of theappended claims. Moreover, in interpreting both the specification andthe claims, all terms should be interpreted in the broadest possiblemanner consistent with the context. In particular, the terms “comprises”and “comprising” should be interpreted as referring to elements,components, or steps in a non-exclusive manner, indicating that thereferenced elements, components, or steps may be present, or utilized,or combined with other elements, components, or steps that are notexpressly referenced. Where the specification claims refers to at leastone of something selected from the group consisting of A, B, C . . . andN, the text should be interpreted as requiring only one element from thegroup, not A plus N, or B plus N, etc.

What is claimed is:
 1. A method of training a networked system toidentify an aspect of a target, comprising: using a first sensor toderive first sensor data from an environment having the target; using alocal AI system executed by at least one processor to analyze the firstsensor data to make a first effort to identify the aspect of the target;determining whether the first effort satisfies a first criterion; and inthe event that the first effort fails to satisfy the first criterion:providing at least some of the first sensor data as an input to a globalAI system; and using the global AI system executed by at least onesecond processor to make a second effort to identify the aspect of thetarget using at least one of the first criterion or a second criterion;in the event that the second effort fails to satisfy the at least one ofthe first criterion or the second criterion, utilizing the global AIsystem to instruct a second sensor to provide second sensor data withrespect to the target, wherein the step of utilizing the global AIsystem to instruct the second sensor comprises instructing an actuatorsystem to direct a movement of the second sensor; and using the globalAI system to make a third effort to identify the aspect of the target.2. The method of claim 1, wherein the actuator system comprises a flyingdrone.
 3. The method of claim 1, wherein the second sensor comprises acamera coupled to a flying drone.
 4. The method of claim 1, furthercomprising: in the event that the second effort satisfies the secondcriterion, providing information to the local AI system to assist thelocal AI system in a future identification of the aspect of the target.5. The method of claim 1, wherein the target is a seal, and the aspectis a sequence of digits displayed on the seal.
 6. The method of claim 1,wherein the target comprises a seal affixed to a motor vehicle.
 7. Themethod of claim 6, wherein the motor vehicle is moving while the localAI system is making the first effort to identify the aspect of thetarget.
 8. The method of claim 1, wherein at least one of the first andsecond criterion comprises a reliability criterion.
 9. The method ofclaim 1, wherein at least one of the first and second criterioncomprises an accuracy criterion.
 10. The method of claim 1, wherein theenvironment at least partially obscures the target.